Chapter 1. Machine Learning Review
Recent years have seen the revival of artificial intelligence (AI) and
machine learning in particular, both in academic circles and the industry. In
the last decade, AI has seen dramatic successes that eluded practitioners in
the intervening years since the original promise of the field gave way to
relative decline until its re-emergence in the last few years.
What made these successes possible, in large part, was the impetus provided
by the need to process the prodigious amounts of ever-growing data, key
algorithmic advances by dogged researchers in deep learning, and the
inexorable increase in raw computational power driven by Moore's Law.
Among the areas of AI leading the resurgence, machine learning has seen
spectacular developments, and continues to find the widest applicability in an
array of domains. The use of machine learning to help in complex decision
making at the highest levels of business and, at the same time, its enormous
success in improving the accuracy of what are now everyday applications,
such as searches, speech recognition, and personal assistants on mobile
phones, have made its effects commonplace in the family room and the board
room alike. Articles breathlessly extolling the power of deep learning can be
found today not only in the popular science and technology press but also in
mainstream outlets such as The New York Times and The Huffington Post.
Machine learning has indeed become ubiquitous in a relatively short time.
An ordinary user encounters machine learning in many ways in their day-to-
day activities. Most e-mail providers, including Yahoo and Gmail, give the
user automated sorting and categorization of e-mails into headings such as
Spam, Junk, Promotions, and so on, which is made possible using text
mining, a branch of machine learning. When shopping online for products on
e-commerce websites, such as https://www.amazon.com/, or watching
movies from content providers, such as Netflix, one is offered
recommendations for other products and content by so-called recommender
systems, another branch of machine learning, as an effective way to retain
customers.
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